{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:29.453012800Z",
     "start_time": "2025-09-12T00:56:28.266380Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2020-02-28', '2020-02-29', '2020-03-01', '2020-03-02'], dtype='datetime64[ns]', freq='D')"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_index = pd.date_range(\"2020-02-28\", periods=4, freq=\"D\")\n",
    "daily_index"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:29.506657500Z",
     "start_time": "2025-09-12T00:56:29.449993600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2020-01-05', '2020-01-12', '2020-01-19', '2020-01-26'], dtype='datetime64[ns]', freq='W-SUN')"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weekly_index = pd.date_range(\"2020-01-01\", \"2020-01-31\", freq=\"W-SUN\")\n",
    "weekly_index"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:29.614598500Z",
     "start_time": "2025-09-12T00:56:29.501823300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "            visitors\n2020-01-05        21\n2020-01-12        15\n2020-01-19        33\n2020-01-26        34",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>visitors</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-05</th>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>2020-01-12</th>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>2020-01-19</th>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>2020-01-26</th>\n      <td>34</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data=[21, 15, 33, 34],\n",
    "             columns=[\"visitors\"], index=weekly_index)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:29.637586300Z",
     "start_time": "2025-09-12T00:56:29.531647Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_89252\\3948704783.py:4: FutureWarning: YF.download() has changed argument auto_adjust default to True\n",
      "  data = yf.download('600000.SS', start=\"2020-01-01\", end=\"2023-12-31\", group_by=\"ticker\")\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "\n",
      "1 Failed download:\n",
      "['600000.SS']: YFRateLimitError('Too Many Requests. Rate limited. Try after a while.')\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "\n",
    "try:\n",
    "    data = yf.download('600000.SS', start=\"2020-01-01\", end=\"2023-12-31\", group_by=\"ticker\")\n",
    "    data.to_csv(\"600000.SS.csv\")\n",
    "except Exception as e:\n",
    "    print(f\"下载失败: {e}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:33.350879Z",
     "start_time": "2025-09-12T00:56:29.578620500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据下载并保存成功\n"
     ]
    }
   ],
   "source": [
    "import akshare as ak\n",
    "import time\n",
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "class ChinaYFinanceDownloader:\n",
    "    def __init__(self, max_retries=5, initial_delay=2):\n",
    "        self.max_retries = max_retries\n",
    "        self.initial_delay = initial_delay\n",
    "        self.cache = {}  # 简单缓存避免重复请求\n",
    "\n",
    "    def download_with_retry(self, ticker, start, end):\n",
    "        \"\"\"指数退避重试机制，规避速率限制\"\"\"\n",
    "        delay = self.initial_delay\n",
    "        for attempt in range(self.max_retries):\n",
    "            try:\n",
    "                # 优先从缓存获取\n",
    "                cache_key = f\"{ticker}_{start}_{end}\"\n",
    "                if cache_key in self.cache:\n",
    "                    return self.cache[cache_key].copy()\n",
    "\n",
    "                # 发起请求\n",
    "                data = yf.download(\n",
    "                    ticker,\n",
    "                    start=start,\n",
    "                    end=end,\n",
    "                    group_by=\"ticker\",\n",
    "                    progress=False  # 关闭进度条减少请求负载\n",
    "                )\n",
    "                if not data.empty:\n",
    "                    self.cache[cache_key] = data.copy()  # 更新缓存\n",
    "                    return data\n",
    "                else:\n",
    "                    raise ValueError(\"返回数据为空，可能股票代码无效或数据不可用\")\n",
    "\n",
    "            except Exception as e:\n",
    "                print(f\"第 {attempt + 1} 次尝试失败: {e}\")\n",
    "                jitter = random.uniform(0.8, 1.2)  # 随机抖动避免同步重试\n",
    "                time.sleep(delay * jitter)\n",
    "                delay *= 2  # 指数退避\n",
    "\n",
    "        raise Exception(f\"超过最大重试次数（{self.max_retries}），请检查网络或稍后重试\")\n",
    "\n",
    "\n",
    "# 使用示例\n",
    "downloader = ChinaYFinanceDownloader()\n",
    "try:\n",
    "    df = ak.stock_zh_a_hist(symbol=\"000568\", start_date=\"20200101\", end_date=\"20231231\")\n",
    "    df.to_csv(\"000568.csv\", encoding='utf-8-sig')\n",
    "    print(\"数据下载并保存成功\")\n",
    "except Exception as e:\n",
    "    print(f\"最终失败: {e}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T00:56:34.760921300Z",
     "start_time": "2025-09-12T00:56:33.338882600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "            000538  000568  601838\n日期                                \n2020-01-02   89.31   85.45    9.05\n2020-01-03   89.10   85.79    8.95\n2020-01-06   88.06   85.62    8.91\n2020-01-07   88.56   86.00    9.07\n2020-01-08   87.53   84.85    8.88\n...            ...     ...     ...\n2023-12-25   48.56  173.09   10.96\n2023-12-26   48.40  171.14   11.01\n2023-12-27   48.45  170.27   11.06\n2023-12-28   48.98  179.49   11.28\n2023-12-29   49.15  179.42   11.26\n\n[970 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>89.31</td>\n      <td>85.45</td>\n      <td>9.05</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>89.10</td>\n      <td>85.79</td>\n      <td>8.95</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>88.06</td>\n      <td>85.62</td>\n      <td>8.91</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>88.56</td>\n      <td>86.00</td>\n      <td>9.07</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>87.53</td>\n      <td>84.85</td>\n      <td>8.88</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-12-25</th>\n      <td>48.56</td>\n      <td>173.09</td>\n      <td>10.96</td>\n    </tr>\n    <tr>\n      <th>2023-12-26</th>\n      <td>48.40</td>\n      <td>171.14</td>\n      <td>11.01</td>\n    </tr>\n    <tr>\n      <th>2023-12-27</th>\n      <td>48.45</td>\n      <td>170.27</td>\n      <td>11.06</td>\n    </tr>\n    <tr>\n      <th>2023-12-28</th>\n      <td>48.98</td>\n      <td>179.49</td>\n      <td>11.28</td>\n    </tr>\n    <tr>\n      <th>2023-12-29</th>\n      <td>49.15</td>\n      <td>179.42</td>\n      <td>11.26</td>\n    </tr>\n  </tbody>\n</table>\n<p>970 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parts = []\n",
    "for ticker in ['000538', '000568', '601838']:\n",
    "    adj_close = pd.read_csv(f'{ticker}.csv', index_col='日期', parse_dates=['日期'], usecols=['日期', '收盘'])\n",
    "    adj_close = adj_close.rename(columns={'收盘': ticker})\n",
    "    parts.append(adj_close)\n",
    "adj_close = pd.concat(parts, axis=1)\n",
    "adj_close"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T02:42:56.401493Z",
     "start_time": "2025-09-12T02:42:56.338126300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "adj_close = adj_close.dropna()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T02:47:38.069410200Z",
     "start_time": "2025-09-12T02:47:37.911500800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "            000538  000568  601838\n日期                                \n2020-01-02   89.31   85.45    9.05\n2020-01-03   89.10   85.79    8.95\n2020-01-06   88.06   85.62    8.91\n2020-01-07   88.56   86.00    9.07\n2020-01-08   87.53   84.85    8.88\n...            ...     ...     ...\n2023-12-25   48.56  173.09   10.96\n2023-12-26   48.40  171.14   11.01\n2023-12-27   48.45  170.27   11.06\n2023-12-28   48.98  179.49   11.28\n2023-12-29   49.15  179.42   11.26\n\n[970 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>89.31</td>\n      <td>85.45</td>\n      <td>9.05</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>89.10</td>\n      <td>85.79</td>\n      <td>8.95</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>88.06</td>\n      <td>85.62</td>\n      <td>8.91</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>88.56</td>\n      <td>86.00</td>\n      <td>9.07</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>87.53</td>\n      <td>84.85</td>\n      <td>8.88</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2023-12-25</th>\n      <td>48.56</td>\n      <td>173.09</td>\n      <td>10.96</td>\n    </tr>\n    <tr>\n      <th>2023-12-26</th>\n      <td>48.40</td>\n      <td>171.14</td>\n      <td>11.01</td>\n    </tr>\n    <tr>\n      <th>2023-12-27</th>\n      <td>48.45</td>\n      <td>170.27</td>\n      <td>11.06</td>\n    </tr>\n    <tr>\n      <th>2023-12-28</th>\n      <td>48.98</td>\n      <td>179.49</td>\n      <td>11.28</td>\n    </tr>\n    <tr>\n      <th>2023-12-29</th>\n      <td>49.15</td>\n      <td>179.42</td>\n      <td>11.26</td>\n    </tr>\n  </tbody>\n</table>\n<p>970 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_close"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T02:47:53.930697400Z",
     "start_time": "2025-09-12T02:47:53.800628900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 970 entries, 2020-01-02 to 2023-12-29\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   000538  970 non-null    float64\n",
      " 1   000568  970 non-null    float64\n",
      " 2   601838  970 non-null    float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 30.3 KB\n"
     ]
    }
   ],
   "source": [
    "adj_close.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T02:48:02.195798700Z",
     "start_time": "2025-09-12T02:48:02.065872Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "                000538      000568      601838\n日期                                            \n2020-01-02  100.000000  100.000000  100.000000\n2020-01-03   99.764864  100.397894   98.895028\n2020-01-06   98.600381  100.198947   98.453039\n2020-01-07   99.160228  100.643651  100.220994\n2020-01-08   98.006942   99.297835   98.121547",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-02</th>\n      <td>100.000000</td>\n      <td>100.000000</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>2020-01-03</th>\n      <td>99.764864</td>\n      <td>100.397894</td>\n      <td>98.895028</td>\n    </tr>\n    <tr>\n      <th>2020-01-06</th>\n      <td>98.600381</td>\n      <td>100.198947</td>\n      <td>98.453039</td>\n    </tr>\n    <tr>\n      <th>2020-01-07</th>\n      <td>99.160228</td>\n      <td>100.643651</td>\n      <td>100.220994</td>\n    </tr>\n    <tr>\n      <th>2020-01-08</th>\n      <td>98.006942</td>\n      <td>99.297835</td>\n      <td>98.121547</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj_close_sample = adj_close.loc['2019-06':'2020-05']\n",
    "rebased_prices = adj_close_sample / adj_close_sample.iloc[0, :] * 100\n",
    "rebased_prices.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T03:27:22.244143500Z",
     "start_time": "2025-09-12T03:27:22.153196200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Axes: xlabel='日期'>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\PythonStudy\\excel+python\\venv\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "D:\\PythonStudy\\excel+python\\venv\\lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  func(*args, **kwargs)\n",
      "D:\\PythonStudy\\excel+python\\venv\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26085 (\\N{CJK UNIFIED IDEOGRAPH-65E5}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "D:\\PythonStudy\\excel+python\\venv\\lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 26399 (\\N{CJK UNIFIED IDEOGRAPH-671F}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rebased_prices.plot()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T03:28:03.357147600Z",
     "start_time": "2025-09-12T03:28:01.355778400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_89252\\2858662723.py:1: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  end_of_month=adj_close.resample('M').last()\n"
     ]
    },
    {
     "data": {
      "text/plain": "            000538  000568  601838\n日期                                \n2020-01-31   86.60   82.65    8.68\n2020-02-29   78.69   74.80    8.17\n2020-03-31   85.55   73.65    7.53\n2020-04-30   90.32   79.17    7.75\n2020-05-31   88.18   85.30    7.97",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-31</th>\n      <td>86.60</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-29</th>\n      <td>78.69</td>\n      <td>74.80</td>\n      <td>8.17</td>\n    </tr>\n    <tr>\n      <th>2020-03-31</th>\n      <td>85.55</td>\n      <td>73.65</td>\n      <td>7.53</td>\n    </tr>\n    <tr>\n      <th>2020-04-30</th>\n      <td>90.32</td>\n      <td>79.17</td>\n      <td>7.75</td>\n    </tr>\n    <tr>\n      <th>2020-05-31</th>\n      <td>88.18</td>\n      <td>85.30</td>\n      <td>7.97</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_of_month = adj_close.resample('M').last()\n",
    "end_of_month.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T03:45:36.192660700Z",
     "start_time": "2025-09-12T03:45:36.072825300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "            000538  000568  601838\n日期                                \n2020-01-31    86.6   82.65    8.68\n2020-02-01     NaN     NaN     NaN\n2020-02-02     NaN     NaN     NaN\n2020-02-03     NaN     NaN     NaN\n2020-02-04     NaN     NaN     NaN",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-31</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-01</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-02</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-03</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-02-04</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_of_month.resample('D').asfreq().head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T04:06:46.935324800Z",
     "start_time": "2025-09-12T04:06:46.769420Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "            000538  000568  601838\n日期                                \n2020-01-31    86.6   82.65    8.68\n2020-02-07    86.6   82.65    8.68\n2020-02-14    86.6   82.65    8.68\n2020-02-21    86.6   82.65    8.68\n2020-02-28    86.6   82.65    8.68",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>000538</th>\n      <th>000568</th>\n      <th>601838</th>\n    </tr>\n    <tr>\n      <th>日期</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-31</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-07</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-14</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-21</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n    <tr>\n      <th>2020-02-28</th>\n      <td>86.6</td>\n      <td>82.65</td>\n      <td>8.68</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end_of_month.resample('W-FRI').ffill().head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-09-12T04:07:17.820049900Z",
     "start_time": "2025-09-12T04:07:17.713114Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
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